# Copyright (c) Alibaba, Inc. and its affiliates.
"""
Here is another way to register the model, by customizing the get_function.

The get_function just needs to return the model + tokenizer/processor.
"""

from typing import Any, Dict

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

from swift.llm import (
    InferRequest,
    Model,
    ModelGroup,
    ModelInfo,
    ModelMeta,
    PtEngine,
    RequestConfig,
    TemplateMeta,
    register_model,
    register_template,
)

register_template(
    TemplateMeta(
        template_type="custom",
        prefix=["<extra_id_0>System\n{{SYSTEM}}\n"],
        prompt=["<extra_id_1>User\n{{QUERY}}\n<extra_id_1>Assistant\n"],
        chat_sep=["\n"],
    )
)


def get_function(
    model_dir: str,
    model_info: ModelInfo,
    model_kwargs: Dict[str, Any],
    load_model: bool = True,
    **kwargs,
):
    # ref: https://github.com/modelscope/ms-swift/blob/main/swift/llm/model/register.py#L182
    model_config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
    tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
    tokenizer.pad_token_id = tokenizer.eos_token_id
    model = None
    if load_model:
        model = AutoModelForCausalLM.from_pretrained(
            model_dir,
            config=model_config,
            torch_dtype=model_info.torch_dtype,
            trust_remote_code=True,
            **model_kwargs,
        )
    return model, tokenizer


register_model(
    ModelMeta(
        model_type="custom",
        model_groups=[
            ModelGroup(
                [
                    Model(
                        "AI-ModelScope/Nemotron-Mini-4B-Instruct",
                        "nvidia/Nemotron-Mini-4B-Instruct",
                    )
                ]
            )
        ],
        template="custom",
        get_function=get_function,
        ignore_patterns=["nemo"],
        is_multimodal=False,
    )
)

if __name__ == "__main__":
    infer_request = InferRequest(messages=[{"role": "user", "content": "who are you?"}])
    request_config = RequestConfig(max_tokens=512, temperature=0)
    engine = PtEngine("AI-ModelScope/Nemotron-Mini-4B-Instruct")
    response = engine.infer([infer_request], request_config)
    swift_response = response[0].choices[0].message.content

    engine.default_template.template_backend = "jinja"
    response = engine.infer([infer_request], request_config)
    jinja_response = response[0].choices[0].message.content
    assert (
        swift_response == jinja_response
    ), f"swift_response: {swift_response}\njinja_response: {jinja_response}"
    print(f"response: {swift_response}")
